Clinical Practice Guidelines, published by HL7 International / Clinical Decision Support. This guide is not an authorized publication; it is the continuous build for version 2.0.0 built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7/cqf-recommendations/ and changes regularly. See the Directory of published versions

Implementation Computable Knowledge

Once computable representations and expressions of any clinical practice guideline have been developed (as a CPG-IG) they must be implemented and integrated with various clinical and operational information systems (e.g., EHRs, Workflow Apps, Quality and Practice Analytics tools, Quality and Registry Reporting tools, etc.) Three main factors come into play. The first consideration is where and how these computable expressions and artifacts will be executed- natively withing systems of record, in an external reasoning engine, or translated and reimplemented in target system native logic languages (e.g. rules engines). The second consideration, which is related to the first, Is how the inferences or insights (e.g. CaseFeatures, Recommendations/Proposals, Metrics) are to be integrated into existing clinical application ecosystems and/or the same or similar workflows supported by these applications. The third consideration, which further relates to the first two, Is where and how these insights will be manifested in such a way to enable guideline-informed clinical workflows and related healthcare activities. Partially orthogonal to, but just as important as these three considerations, is the ability to assess and ensure conformance of specific implementations of computable clinical practice guidelines using the specifications and requirements outlined in this implementation guide.

While all of these factors Have implications for the overall level of effort, the methods of implementing knowledge and mechanisms of integration are directly reflected in the overall selection of workflow enablements in a given setting. The last two sections further discuss trade-offs between effort, time to develop, and capabilities enabled (and value that can be derived) when developing a CPG. From very basic context for narrative snippets searchable in a library (or Infobutton from EHR) to full clinical workflow enablement, cognitive support, and pathway tracking (likely via SMART-on-FHIR App or deep EHR integration). CPGs also enable feedback loops with real-world evidence of actual guideline usage and outcomes, and provide a substrate for the evidence ecosystem and a feedforward loop for evidence updates. To support description of, and allow declaration of conformance to, these various capabilities enabled by CPGs, the CPG-IG defines levels of enablement that correlate to work effort, time to delivery, and capabilities enabled.

• Methods of Implementing Knowledge (expressions and artifacts)
• Mechanisms of Integration with Clinical Information Systems
• Levels of Workflow Enablement
• Levels of Conformance